Abstract
Computer Vision methods are used to extract information from images and videos, but their contextual elements are not always sufficient to extract correct and accurate information. In these cases, content from other sources and types of data such as audio and text, or other information external to the data, such as a priori knowledge, can be used to complement and enrich the context of the information of interest. Additionally, the application context can impose various restrictions such as hardware limitations, the need to guarantee privacy, among others. Therefore, modern Computer Vision methods need to be able to automatically integrate the contextual elements of the information of interest and also those related to the application in question. The objective of this project is the development of computer vision models and methods that are capable of generating context-rich representations. The project will be organized around three main integrated research lines: (i) Optimum use of unsupervised data; (ii) Alignment of multi-modal domains; (iii) Properties of representations. Of special interest are computer vision applications involving edge devices (edge computing) and mobile devices (such as smartphones and mini-computers). To develop, test and validate the methods, we intend to build an experimental setup consisting of multiple cameras and sensors that will allow the construction of supervised datasets to be explored by the group. (AU)
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